- talks - lists.cs.princeton.edu

Jonathan Ragan-Kelley , TODAY March 22, 12:30pm
by Mitra D. Kelly 22 Mar '16

22 Mar '16

Jonathan Ragan-Kelley, Stanford March 22, 12:30pm Computer Science 105 Title: Organizing Computation for High-Performance Visual Computing Abstract: Future visual computing applications-from photorealistic real-time rendering, to 4D light field cameras, to pervasive sensing and computer vision-demand orders of magnitude more computation than we currently have. >From data centers to mobile devices, performance and energy scaling is limited by locality (the distance over which data has to move, e.g., from nearby caches, far away main memory, or across networks) and parallelism. Because of this, I argue that we should think of the performance and efficiency of an application as determined not just by the algorithm and the hardware on which it runs, but critically also by the organization of computations and data. For algorithms with the same complexity-even the exact same set of arithmetic operations and data-executing on the same hardware, the order and granularity of execution and placement of data can easily change performance by an order of magnitude because of locality and parallelism. To extract the full potential of our machines, we must treat the organization of computation as a first class concern while working across all levels from algorithms and data structures, to compilers, to hardware. This talk will present facets of this philosophy in systems I have built for visual computing applications from image processing and vision, to 3D rendering, simulation, optimization, and 3D printing. I will show that, for data-parallel pipelines common in graphics, imaging, and other data-intensive applications, the organization of computations and data for a given algorithm is constrained by a fundamental tension between parallelism, locality, and redundant computation of shared values. I will focus particularly on the Halide language and compiler for image processing, which explicitly separates what computations define an algorithm from the choices of organization which determine parallelism, locality, memory footprint, and synchronization. I will show how this approach can enable much simpler programs to deliver performance often many times faster than the best prior hand-tuned C, assembly, and CUDA implementations, while scaling across radically different architectures, from ARM cores, to massively parallel GPUs, to FPGAs and custom ASICs. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Princeton ACM/IEEE-CS April meeting
by Nicole E. Wagenblast 22 Mar '16

22 Mar '16

The New ABCs of Research: Achieving Breakthrough Collaborations Thursday, April 21, 2016, 8:00pm Refreshments and networking at 7:30pm Computer Science 105 How should we organize research programs in computer science and technology? Research helps us produce innovative new products (the next iPhone), and research also delivers “foundational theories” (new algorithms and supporting technologies). Solving the immense problems of the 21st century will require ambitious research teams that are skilled at producing practical solutions and foundational theories simultaneously – that is the ABC Principle: Applied & Basic Combined. Then these research teams can deliver high-impact outcomes by applying the SED Principle: Blend Science, Engineering and Design Thinking, which encourages use of the methods from all three disciplines. These guiding principles (ABC & SED) are meant to replace Vannevar Bush’s flawed linear model from 1945 that has misled researchers for 70+ years. These new guiding principles will enable students, faculty, business leaders, and government policy makers to accelerate discovery and innovation. The examples in the talk will emphasize how these guiding principles can be applied to reinvigorate computing research. Ben Shneiderman (http://www.cs.umd.edu/~ben) is a Distinguished University Professor in the Department of Computer Science at the University of Maryland. He is also the Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/), and a Member of the University of Maryland Institute for Advanced Computer Studies (UMIACS). He is a Fellow of the AAAS, ACM, IEEE, and NAI, and a Member of the National Academy of Engineering, in recognition of his pioneering contributions to human-computer interaction and information visualization. His contributions include the direct manipulation concept, clickable highlighted web-links, touchscreen keyboards, dynamic query sliders for Spotfire, development of treemaps, novel network visualizations for NodeXL, and temporal event sequence analysis for electronic health records. Ben is the co-author with Catherine Plaisant of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016). His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. Shneiderman’s latest book is The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, February 2016). Information: Dennis Mancl (908) 285-1066 On-line info: http://PrincetonACM.acm.org All Princeton ACM / IEEE-CS meetings are open to the public. Students and their parents are welcome. There is no admission charge, and refreshments are served. A pre-meeting dinner is held at 6:00 p.m. at Ruby Tuesday’s Restaurant on Route 1. Please send email to princetonacm(a)gmail.com in advance if you plan to attend the dinner.

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4:30pm Thu Mar 24 talk on automatic synthesis in cyberphysical systems in E-Quad B205
by Jennifer Rexford 21 Mar '16

21 Mar '16

> > > Speaker: > Vasumathi Raman, United Technologies Research Center > Title: > Synthesis for Autonomy: Connecting the Physical, the Logical and the Human > Day: > Thursday, March 24, 2016 > > > Time: > 4:30 pm > Room: > B205, E-Quad > Host: > Prof. Antoine Kahn > > > > > > > > > Abstract: > Autonomous cyber-physical systems like unmanned aircraft and self-driving cars stretch the limits of engineering, and reasoning precisely about their behavior is increasingly difficult. Moreover, a correct implementation is often elusive even when the desired outcome is obvious. Algorithmic synthesis makes the formal specification of desired behavior an integral part of the controller design process -- the resulting controllers are correct by construction. Synthesis enables non-expert human users to provide complex system specifications, and be assured of their fulfillment. However, this goal of automatically generating autonomous control from user-defined specifications poses a host of unprecedented challenges. > > In this talk, I will present two frameworks for automated synthesis from high-level specifications. I will first describe a system for generating, troubleshooting, and executing controllers for autonomous robots from natural language specifications, including analytical tools for automatically determining causes of failure to synthesize. I will then introduce an optimization-based controller-synthesis framework for high-dimensional systems operating in uncertain environments, bypassing the need for discretizing the state space and admitting specifications that govern not only the order of events, but also their timing. Finally, I will present new research directions that push the envelope of synthesis for autonomous systems. > > Bio: > Vasu Raman is a Senior Scientist at the United Technologies Research Center in Berkeley, CA. Her research explores algorithmic methods for designing and controlling autonomous cyber-physical systems, guaranteeing correctness with respect to user-defined specifications. She earned a PhD in Computer Science from Cornell University in 2013, and was a postdoctoral scholar in the Department of Computing and Mathematical Sciences and the Department of Control and Dynamical Systems at the California Institute of Technology from 2013-2015. She has contributed extensively to the LTLMoP toolkit for robot mission planning and the slugs reactive synthesis tool, and developed BluSTL, a toolbox for automatically generating controllers from timed temporal logic specifications. Vasu also holds a BA in Computer Science and Mathematics from Wellesley College.

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Naga Katta will present his Pre-FPO Thursday, March 24, 2016 at 10am in CS 402.
by Nicki Gotsis 21 Mar '16

21 Mar '16

Naga Katta will present his Pre-FPO Thursday, March 24, 2016 at 10am in CS 402. The members of his committee are as follows: Jennifer Rexford (Adviser), Mike Freedman (Reader), Dave Walker (Reader), Nick Feamster (Non-reader) and Aarti Gupta (Non-reader). Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Building reliable and efficient software-defined networks Abstract: Software-defined networking (SDN) promises flexible network control by enforcing fine-grained policies in the dataplane through a centralized controller. However, despite this promise, operators accustomed to fast and fault-tolerant routing using traditional protocols find two important properties lacking in SDNs - efficiency and reliability. In particular, they face the following problems while deploying SDN - One, the control plane timescales are too slow to enforce fault-tolerant load balancing in order to efficiently use the available network capacity. Second, the commodity SDN switches have limited space to enforce fine-grained policy rules which undermines the promise of flexible control. Third, the centralized controller itself is a single point of failure which is unacceptable for operators used to running distributed fault-tolerant network protocols. My thesis aims to mitigate these problems using novel algorithms that exploit advanced data plane capabilities and enhancements to the control plane software. At the same time, we also provide simple abstractions on top of these systems so that network operators writing control programs need not worry about low-level details of how these problems are dealt with. First, I will present HULA, which gives the abstraction of one big efficient non-blocking switch. Instead of asking the control plane to choose the best path for each new flow, HULA efficiently routes traffic on least congested paths in the dataplane. HULA uses advanced hardware data plane capabilities to infer global congestion information and uses that information to do fine-grained load balancing at RTT timescales. HULA is congestion-aware, scales to large topologies and is robust to topology failures. Second, I will present CacheFlow which helps enforcing fine-grained policies by proposing the abstraction of a switch with logically infinite rule space. CacheFlow uses a combination of software and hardware data paths to bring the best of both worlds to policy enforcement. By dynamically caching a small number of heavy hitting rules in the hardware switch and the rest of the rules in the software data path, it achieves both high throughput and high rule capacity. Since cross-rule dependencies make rule caching difficult, CacheFlow uses novel algorithms to do dependency-aware, efficient and transparent rule caching. Finally, I will present Ravana which gives the abstraction of one logically centralized controller. Given this abstraction, the network operator only writes programs for one controller and the Ravana runtime takes care of replicating the control logic for fault-tolerance. Since network switches carry additional state external to the controller state, Ravana uses an enhanced version of traditional replicated state machine protocols to ensure ordered and exactly-once execution of network events. Together these systems propose a new architecture where basic routing is done efficiently at dataplane timescales, policy enforcement is done scalably with the help of software data planes and the control plane is fault-tolerant. This new architecture has the properties of fast routing and fault-tolerance of traditional networks while delivering the promise of efficient enforcement of fine-grained control policies.

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Colloquium Speaker Stefanie Mueller, Monday March 28th, 12:30pm
by Mitra D. Kelly 21 Mar '16

21 Mar '16

Colloquium Speaker Stefanie Mueller, Human-Computer Interaction Lab at Hasso Plattner Institute Monday March 28th, 12:30pm Computer Science 105 Talk Title: Interacting with Personal Fabrication Machines Abstract: In anticipation of 3D printers reaching millions of users, I am investigating how to allow future users to interact with the new hardware. I present a series of interactive software+hardware systems that I created to answer this question. They are characterized by two main properties. First, they produce physical output quickly, allowing users not only to see their results, but also to touch and test their mechanical properties as users work towards a solution. Second, the systems allow users to interact directly with the workpiece, i.e., rather than using a digital 3D editor, users manipulate the workpiece located inside the 3D printer by pointing at it, causing the machine to then modify the workpiece accordingly. I put my research into perspective by drawing analogies to the evolution of interactive computing from batch processing, to turn taking, to direct manipulation. Bio: Stefanie Mueller is a PhD student working with Patrick Baudisch at the Human-Computer Interaction Lab at Hasso Plattner Institute. In her research, she develops novel hardware and software systems that advance personal fabrication technologies. Stefanie has published 10 papers at the most selective HCI venues CHI and UIST, for which she received a best paper award and two best paper nominees. She is also serving on the CHI and UIST program committees as an associate chair. In addition, Stefanie has been an invited speaker at universities and research labs, such as MIT, Stanford, UC Berkeley, Microsoft Research, Disney Research, and Adobe Research. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Jonathan Ragan-Kelley , Tuesday March 22, 12:30pm
by Mitra D. Kelly 21 Mar '16

21 Mar '16

Jonathan Ragan-Kelley, Stanford March 22, 12:30pm Computer Science 105 Title: Organizing Computation for High-Performance Visual Computing Abstract: Future visual computing applications-from photorealistic real-time rendering, to 4D light field cameras, to pervasive sensing and computer vision-demand orders of magnitude more computation than we currently have. >From data centers to mobile devices, performance and energy scaling is limited by locality (the distance over which data has to move, e.g., from nearby caches, far away main memory, or across networks) and parallelism. Because of this, I argue that we should think of the performance and efficiency of an application as determined not just by the algorithm and the hardware on which it runs, but critically also by the organization of computations and data. For algorithms with the same complexity-even the exact same set of arithmetic operations and data-executing on the same hardware, the order and granularity of execution and placement of data can easily change performance by an order of magnitude because of locality and parallelism. To extract the full potential of our machines, we must treat the organization of computation as a first class concern while working across all levels from algorithms and data structures, to compilers, to hardware. This talk will present facets of this philosophy in systems I have built for visual computing applications from image processing and vision, to 3D rendering, simulation, optimization, and 3D printing. I will show that, for data-parallel pipelines common in graphics, imaging, and other data-intensive applications, the organization of computations and data for a given algorithm is constrained by a fundamental tension between parallelism, locality, and redundant computation of shared values. I will focus particularly on the Halide language and compiler for image processing, which explicitly separates what computations define an algorithm from the choices of organization which determine parallelism, locality, memory footprint, and synchronization. I will show how this approach can enable much simpler programs to deliver performance often many times faster than the best prior hand-tuned C, assembly, and CUDA implementations, while scaling across radically different architectures, from ARM cores, to massively parallel GPUs, to FPGAs and custom ASICs. Bio: Jonathan Ragan-Kelley is a postdoc in computer science at Stanford. He works on high-efficiency visual computing, including systems, compilers, and architectures for image processing, vision, 3D rendering, 3D printing, physical simulation, and scientific computing. He earned his PhD in Computer Science at MIT in 2014, where he built the Halide language for high-performance image processing. Halide is now used throughout industry to deploy code to hundreds of millions of smartphones and process tens of billions of images per day. Jonathan previously built the Lightspeed preview system, which was used on over a dozen films at Industrial Light & Magic and was a finalist for an Academy Technical Achievement Award. He has worked in GPU architecture, compilers, and research at NVIDIA, Intel, and ATI. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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Guangda Hu will present his Pre FPO on Friday, March 25, 2016 at 10:30am in CS 301.
by Nicki Gotsis 21 Mar '16

21 Mar '16

Guangda Hu will present his Pre FPO on Friday, March 25, 2016 at 10:30am in CS 301. The members of his committee are: Zeev Dvir (adviser), Readers: Shubhangi Saraf (Rutgers), Sanjeev Arora; Non-readers: Mark Braverman, Elad Hazan. Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Results on the complexity of codes with nice recovering capabilities Abstract: In both practice and theory, much research has been done on error correcting codes with nice recovering capabilities. In this thesis, we will discuss two types of such codes, namely Locally Decodable/Correctable Codes (LDCs/LCCs) and Maximally Recoverable (MR) Codes. LDCs are error correcting codes that each symbol of the original message can be recovered by making a small number of queries even if a constant fraction of the codeword is corrupted. LCCs are similar to LDCs except that we can correct every symbol in the codeword and not just the original message. LDCs/LCCs play an important role in theoretical computer science but are still not well understood. For the case that the query number is very small (constant greater than 2 for example), the best known constructions are the Matching-Vector codes [DGY11] which have subexponential (but superpolynomial) code lengths, and the best lower bound is super quadratic for 3-LCC over reals [DSW14]. It is a great challenge to make this huge gap smaller. We will discuss two results towards better understandings about the lower bounds of constant query LDCs/LCCs. First, we consider codes over real numbers and discuss an approximate version of LDCs. The problem of approximate codes itself is interesting and it is also related to notion of [DSW14] which gives the first super quadratic lower bound for 3-LCCs. We obtain bounds for approximate codes that are similar to the best known ones for exact codes. Second, we consider a generalization of the Sylvester-Gallai theorem in geometry. Basically LCCs and the Sylvecter-Gallai theorem are both questions about configurations of points with many local dependencies, and the connection between them has been studied in several previous works. We give a subspace version of Sylvester-Gallai and generalize an important tool used in [DSW14]. In distributed data storage systems, one common practice is to partition data into local groups and there are both local checksums for each local group and global checksums that depend on all data. MR codes are systematic codes that have the information theoretically optimal erasure recovering capability. It is known that there are explicit constructions for MR codes in any common reasonable topology of group partitioning [GHJY14]. However, the problem is that all known constructions require the alphabet to be very large and this is inefficient for both storing and recovering. In practice, it is actually observed that there is a tradeoff between "efficiency" and "reliability", though there was no theoretical proof. We give the first lower bound for the alphabet size of MR codes of a common topology. This shows that a storage system that is maximally recoverable cannot be efficient and is the first proof of the tradeoff. We also give new constructions with new techniques of MR codes that improves the previous results for various parameters. Based on joint works with Jop Briet, Zeev Dvir, Parikshit Gopalan, Swastik Kopparty, Shubhangi Saraf, Carol Wang, Sergey Yekhanin.

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Colloquium Speaker: Igor Mordatch, Wednesday, March 23, 4:30pm- reminder
by Nicole E. Wagenblast 18 Mar '16

18 Mar '16

Colloquium Speaker Igor Mordatch, University of California, Berkeley Wednesday, March 23, 4:30pm Computer Science 105 Automated Discovery and Learning of Complex Movement Behaviours In order to create truly autonomous physical robots, understand the underlying principles behind human movement, or tell narratives in animated films and interactive games, it is necessary to synthesize movement behaviours with the same wide variety, richness and complexity observed in humans and other animals. Moreover, these behaviours should be discovered automatically from only a few core principles, and not be a result of extensive manual engineering or a mimicking of demonstrations. In this talk at the intersection of robotics, computer graphics and biomechanics, I will show work on novel trajectory and policy optimization methods that give rise to a range of behaviours such getting up, climbing, moving objects, hand manipulation, acrobatics, and various cooperative actions involving multiple characters all in a single system. The resulting movements can be used to successfully control a physical bipedal robot and coupled with detailed models of human physiology, motions that match real human motion can be produced de novo, giving the predictive power to conduct virtual biomechanics experiments. The approach is fully automatic, based on general neural network policy representations and does not require domain knowledge specific to each behaviour, pre-existing examples or motion capture data. Although discovery and learning are computationally-expensive and rely on cloud and GPU computing, the interactive animation can run in real-time on any hardware once the controllers are learned. Igor Mordatch is a post-doctoral fellow working with professor Pieter Abbeel at University of California, Berkeley. He received his PhD at University of Washington under supervision of Emanuel Todorov and Zoran Popovic and undergraduate degree in Computer Science and Mathematics at University of Toronto. He worked as a visiting researcher at Stanford University and Pixar Research. His research interests lie in the development and use of optimal control and machine learning techniques for robotics, computer graphics, and biomechanics.

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Colloquium Speaker: Mayank Goel Friday, March 25- 12:30pm
by Nicole E. Wagenblast 17 Mar '16

17 Mar '16

Colloquium Speaker Mayank Goel , University of Washington Friday, March 25, 2016 - 12:30pm Computer Science 105 Teaching Old Sensors New Tricks A fundamental issue with any new sensing technology is the deployment burden. The new technology often comes with a rigid and perhaps expensive list of pre-requisites; and it affects the overall cost-effectiveness, deployability, and accessibility of the solution. For example, a user might find it hard to buy and carry a medical device with them everyday. The primary theme of my Ph.D. research has been to reduce the adoption and deployment barriers of new sensing technologies by using the sensors that are already around us. In this talk, I will present my research on extending the capabilities of the on-board sensors on consumer devices for various applications. First, I will discuss how we can leverage the on-device sensors to enable novel human-computer interactions and make the mobile devices more usable. Next, I will discuss my work on using the on-device sensors for building health technologies that work as well as their clinically-approved counterparts and they continue to evolve through deployments in various parts of the world. Finally, I will discuss how my recent work in extending the on-device sensors is shaping my future interests and how I plan to continue to lower the adoption barriers for new sensing technologies. Mayank Goel is a Ph.D. candidate in Computer Science and Engineering in the Ubiquitous Computing (UbiComp) Lab at the University of Washington, advised by Shwetak Patel and Gaetano Borriello. His research focuses on building sensing systems to solve hard problems in diverse application areas, ranging from health sensing, technologies for the developing world to novel user interaction; with an eye toward reducing deployment barriers. His research in the area of health technologies is currently used by hundreds of patients every week, around the world. He was awarded the Microsoft Research Ph.D. Fellowship in 2014, a University of Washington CSE Fellowship, and 7 Best Paper Nominations. He received an M.S. in Computer Science from Georgia Institute of Technology in 2009, where he was advised by Gregory Abowd and specialized in mobile and ubiquitous computing.

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Colloquium Speaker, Jonathan Ragan-Kelley, March 22, 12:30pm
by Mitra D. Kelly 16 Mar '16

16 Mar '16

Colloquium Speaker Jonathan Ragan-Kelley, Stanford March 22, 12:30pm Computer Science 105 Organizing Computation for High-Performance Visual Computing Future visual computing applications-from photorealistic real-time rendering, to 4D light field cameras, to pervasive sensing and computer vision-demand orders of magnitude more computation than we currently have. >From data centers to mobile devices, performance and energy scaling is limited by locality (the distance over which data has to move, e.g., from nearby caches, far away main memory, or across networks) and parallelism. Because of this, I argue that we should think of the performance and efficiency of an application as determined not just by the algorithm and the hardware on which it runs, but critically also by the organization of computations and data. For algorithms with the same complexity-even the exact same set of arithmetic operations and data-executing on the same hardware, the order and granularity of execution and placement of data can easily change performance by an order of magnitude because of locality and parallelism. To extract the full potential of our machines, we must treat the organization of computation as a first class concern while working across all levels from algorithms and data structures, to compilers, to hardware. This talk will present facets of this philosophy in systems I have built for visual computing applications from image processing and vision, to 3D rendering, simulation, optimization, and 3D printing. I will show that, for data-parallel pipelines common in graphics, imaging, and other data-intensive applications, the organization of computations and data for a given algorithm is constrained by a fundamental tension between parallelism, locality, and redundant computation of shared values. I will focus particularly on the Halide language and compiler for image processing, which explicitly separates what computations define an algorithm from the choices of organization which determine parallelism, locality, memory footprint, and synchronization. I will show how this approach can enable much simpler programs to deliver performance often many times faster than the best prior hand-tuned C, assembly, and CUDA implementations, while scaling across radically different architectures, from ARM cores, to massively parallel GPUs, to FPGAs and custom ASICs. Jonathan Ragan-Kelley is a postdoc in computer science at Stanford. He works on high-efficiency visual computing, including systems, compilers, and architectures for image processing, vision, 3D rendering, 3D printing, physical simulation, and scientific computing. He earned his PhD in Computer Science at MIT in 2014, where he built the Halide language for high-performance image processing. Halide is now used throughout industry to deploy code to hundreds of millions of smartphones and process tens of billions of images per day. Jonathan previously built the Lightspeed preview system, which was used on over a dozen films at Industrial Light & Magic and was a finalist for an Academy Technical Achievement Award. He has worked in GPU architecture, compilers, and research at NVIDIA, Intel, and ATI. Mitra Kelly Academic Secretary Princeton University Computer Science Dept 35 Olden Street Princeton NJ 08540 mkelly(a)cs.princeton.edu <mailto:mkelly@cs.princeton.edu> 609-258-4562

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